Automated Classification of Software Bug Reports

Otoom, Ahmed Fawzi; Al-jdaeh, Sara; Hammad, Maen

doi:10.1145/3357419.3357424

Cited by 24 publications

(14 citation statements)

References 13 publications

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“…Pandey et al (2018) propose to use the TFM of the issue titles as input for NB, Support Vector Machine (SVM), or LR classifiers. Otoom et al (2019) propose to use a variant of the TFM with a fixed word set. They use a list of 15 keywords related to non-bug issues (e.g., enhancement, improvement, refactoring) and calculate the term frequencies for them based on the title and description of the issue.…”

Section: Supervised Approachesmentioning

confidence: 99%

“…In general, our proposed approach is in line with the related work, i.e., we also either rely on a standard text processing pipeline based on the TFM and IDF as input for common classification models or use the fastText algorithm which directly combines the different aspects. The approaches by Otoom et al (2019) and Zolkeply and Shao (2019) try to incorporate manually specified knowledge into the learning process through curated keyword lists. Our approach to incorporate knowledge is a bit different, because we rather rely on rules that specify different training data sets and do not restrict the feature space.…”

Section: Supervised Approachesmentioning

confidence: 99%

“…We clearly separated the training and test data, to avoid any kind of information leakage, e.g., because timing aspects were not considered while doing cross-validation. When we looked at the literature, all prior publications performed some sort of train/test split within the projects, sometimes with accounting for the timing (Terdchanakul et al 2017;Otoom et al 2019;Pingclasai et al 2013), but sometimes not Limsettho et al (2014a), Chawla and Singh (2015), and Qin and Sun (2018).…”

Section: Comparison With the Literaturementioning

confidence: 99%

“…Researchers suggested an alternative through the automated classification of issue types by analyzing the issue titles and descriptions with unsupervised machine learning based on clustering the issues Limsettho et al (2014b), Hammad et al (2018) and Chawla and Singh (2018) and supervised machine learning that create classification models (Antoniol et al 2008;Pingclasai et al 2013;Limsettho et al 2014a;Chawla and Singh 2015;Zhou et al 2016;Terdchanakul et al 2017;Pandey et al 2018;Qin and Sun 2018;Zolkeply and Shao 2019;Otoom et al 2019;Kallis et al 2019). There are two possible use cases for such automated classification models.…”

Section: Introductionmentioning

confidence: 99%

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On the feasibility of automated prediction of bug and non-bug issues

Herbold

Trautsch

2020

Empir Software Eng

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Context Issue tracking systems are used to track and describe tasks in the development process, e.g., requested feature improvements or reported bugs. However, past research has shown that the reported issue types often do not match the description of the issue. Objective We want to understand the overall maturity of the state of the art of issue type prediction with the goal to predict if issues are bugs and evaluate if we can improve existing models by incorporating manually specified knowledge about issues. Method We train different models for the title and description of the issue to account for the difference in structure between these fields, e.g., the length. Moreover, we manually detect issues whose description contains a null pointer exception, as these are strong indicators that issues are bugs. Results Our approach performs best overall, but not significantly different from an approach from the literature based on the fastText classifier from Facebook AI Research. The small improvements in prediction performance are due to structural information about the issues we used. We found that using information about the content of issues in form of null pointer exceptions is not useful. We demonstrate the usefulness of issue type prediction through the example of labelling bugfixing commits. Conclusions Issue type prediction can be a useful tool if the use case allows either for a certain amount of missed bug reports or the prediction of too many issues as bug is acceptable.

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Section: Supervised Approachesmentioning

confidence: 99%

Section: Supervised Approachesmentioning

confidence: 99%

Section: Comparison With the Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the feasibility of automated prediction of bug and non-bug issues

Herbold

Trautsch

2020

Empir Software Eng

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“…Many studies exist that address the issues related to bug reports [13]. These include bug categorization [14]- [19], bug prioritization [20]- [23], bug localization [24], bug assignment [25], bug classification [10], [26], [27], bug severity prediction [28], and bug report summarization [29]- [31].…”

Section: Introductionmentioning

confidence: 99%

CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms

2021

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Bug reports facilitate software development teams in improving the quality of software. These reports include significant information related to problems encountered within a software, possible enhancement suggestions, and other potential issues. Bug reports are typically complex and are too detailed; hence a lot of resources are required to analyze and process them manually. Moreover, it leads to delays in the resolution of high priority bugs. Accurate and timely processing of bug reports based on their category and priority plays a significant role in improving the quality of software maintenance. Therefore, an automated process of categorization and prioritization of bug reports is needed to address the aforementioned issues. Automated categorization and prioritization of bug reports have been explored recently by many researchers; however, limited progress has been made in this regard. In this research, we present a novel framework, titled CaPBug, for automated categorization and prioritization of bug reports. The framework is implemented using Natural Language Processing (NLP) and supervised machine learning algorithms. A baseline corpus is built with six categories and five prioritization levels by analyzing more than 2000 bug reports of Mozilla and Eclipse repository. Four classification algorithms i.e., Naive Bayes, Random Forest, Decision Tree, and Logistic Regression have been used to categorize and prioritize bug reports. We demonstrate that the CaPBug framework achieved an accuracy of 88.78% by using a Random Forest classifier with a textual feature for predicting the category. Similarly, using the CaPBug framework, an accuracy of 90.43% was achieved in predicting the priority of bug reports. Synthetic Minority Over-Sampling Technique (SMOTE) has been applied to address the class imbalance issue in priority classes.

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LLM-BRC: A large language model-based bug report classification framework

Du,

Liu,

et al. 2024

Software Qual J

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Automated Classification of Software Bug Reports

Cited by 24 publications

References 13 publications

On the feasibility of automated prediction of bug and non-bug issues

On the feasibility of automated prediction of bug and non-bug issues

CaPBug-A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms

LLM-BRC: A large language model-based bug report classification framework

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